Occurrence and temporal overlap of sympatric jungle cats and leopard cats in Parsa‒Koshi Complex, Nepal

Co-occurrence and spatial and temporal overlap of sympatric jungle and leopard cats are influenced by habitat preferences, and interspecific competition. Understanding these factors influence is crucial for developing effective conservation strategies. We conducted a camera survey in Parsa‒Koshi Complex (PKC), Nepal during December 2022–March 2023 to investigate factors influencing occupancy and spatial and temporal overlap between jungle cats (Felis chaus) and leopard cats (Prionailurus bengalensis). The mean detection probability (t = 0.664, p = 0.507) did not differ between jungle cats (p = 0.500 ± 0.289) and leopard cats (p = 0.501 ± 0.288); however, occupancy (t = 31.008, p < 0.001) was greater for jungle cats (ψ = 0.247 ± 0.020) than leopard cats (ψ = 0.178 ± 0.019). Jungle cats and leopard cats were positively associated with large predators, and jungle cats were positively associated with human presence and negatively associated with canopy cover. We observed high diel overlap between leopard cats and jungle cats (Dhat1 = 0.802, norm0CI: 0.720–0.884), with both species largely nocturnal. Co-existence of jungle cats and leopard cats in PKC appears to be facilitated by spatial segregation. These findings provide valuable insights into the complex ecological dynamics and interactions between sympatric jungle and leopard cats.

these cat species underscores the importance of resource partitioning and highlights the complexity of predator interactions within ecosystems.
The presence of larger predators can have a notable influence on jungle cats and leopard cats 27,28 .Small carnivores generally exhibit spatial or temporal avoidance to reduce encounters with larger carnivores 29 .Temporal habitat segregation has also been identified as a mechanism to allow co-occurrence of diverse predator guilds 30,31 .Larger predator species can potentially create a competitive dynamic with jungle cats and leopard cats exhibiting spatial and temporal avoidance 32,33 .In addition, the coexistence of humans with these cat species can reduce human-rodent conflicts by their hunting of prey in human-occupied areas 34 .However, loss and fragmentation of natural habitats from human activities can restrict their movements, disrupt foraging patterns, and limit access to prey.Human activities also result in potential threats to jungle cats and leopard cats including poaching and trapping 35 and collisions with vehicles [36][37][38] which can directly impact populations.
We aimed to assess the occupancy patterns of sympatric jungle and leopard cats and identify the drivers shaping it in Parsa-Koshi Complex (PKC), Nepal, to provide baseline information for informing conservation strategies including habitat management and protected area planning.The PKC includes protected and nonprotected areas providing greater opportunities to understand their occupancy in different management regimes.Further we investigated the difference in temporal activity between these sympatric species in PKC.

Results
We obtained 10,352 images including 3969 of wild mammals.We obtained 81 jungle cat detections at 51 sites and 68 leopard detections at 44 sites; both species were detected at 19 sites.The mean probability of large carnivore detected together was 0.357 ± 0.481(SD), mean number of human detections was 63.1 ± 237.9 (SD), and mean number of livestock detections 36.46 ± 102.17 (SD).Mean canopy cover was 41.6 ± 21.5% across camera locations whereas the mean distance to water body was 2196 ± 2222 m, mean distance to major road was 795 ± 1252 m, and mean distance to nearest human settlement was 3211 ± 1932 m.
Probability of occupancy increased for jungle cats and leopard cats with increasing detections of large predators (Table 1, Fig. 1).Jungle cat occupancy decreased with increasing canopy cover (βcanopycover = -0.73 1 ± 0.341).No other factors influenced occupancy of jungle cats or leopard cats.We observed overall modest but greater detection probability of jungle cats and leopard cats in eastern PKC, with few areas of PKC having high (> 0.50) detection probability for either species (Fig. 2).

Discussion
We found that large predators, canopy cover and frequency of human detections influenced the occupancy of jungle cats or leopard cats.The presence of large predators such as tigers and leopards can influence on jungle cats and leopard cats within shared habitats 31,39 .Consequently, the presence of large carnivores can shape the behavior, habitat selection, and distribution of jungle cats and leopard cats, potentially leading to coexistence through spatial or temporal niche differentiation within the carnivore community [29][30][31]40 . Preence of these small cats in the same habitat as large carnivores might be due to scavenging of carcass remains from large carnivore kills 22,41 .
The jungle cat occupancy decreased with increasing canopy cover which might be due to patchy landscape, potentially resulting from habitat fragmentation and such variability in canopy cover was noticed in the PKC.
Jungle cats prefer open areas, probably due to improved ability to hunt smaller rodents 24 .Further, the tufted ear tips, slender body, long limbs, short tails and cream-colored pelage of jungle cats provide better adaptive advantage in open areas 42 .Canopy cover did not influence the occupancy of leopard cats probably due to this species being a habitat generalist 20 .However, leopard cats prefer dense canopy cover, probably due to presence of small prey species such as rodents, birds, reptiles, frogs, and insects 43 .Among forests, they appear to select disturbed forests and plantations 44 .Leopard cats reportedly used forests with 75-83% canopy cover in Singapore 43 .
The detection probability and occupancy of both cats was not influenced by nearness to water bodies, however, the proximity of water bodies may influence distributions of these species at a different spatial resolution, as they serve as important resources for jungle cats and leopard cat's prey species for hunting near to bed reeds of riverbanks and dry streams 22 .In our study, distance to nearest settlement, major road, and number of livestock detections or detections of the other cat species did not influence occupancy of leopard cats or jungle cats.It might be due to fragmented patches of habitat between dense settlement and roads in the study area.Fragmented habitat from human settlements and roads in the study area could have caused the observed response.Habitat fragmentation can alter species movements and behavior (e.g., dispersal) through limiting available resources from widespread and abundant to localized in isolated patches (i.e.clumped resources), and mitigate the effects of nearby human activities 45 .The insignificant impact of sympatric cat species on each other could occur through spatial niche segregation or variation in habitat selection where jungle cats select more open areas 19,46 and leopard cats forests with dense canopy 43 .Diet activity of jungle cats and leopard cats revealed high overlap between the two species.Both cat species exhibited greater activity during early morning and evening and were largely inactive during the day.These activity patterns may be influenced by factors such as hunting behavior, prey availability, and avoidance of  www.nature.com/scientificreports/human disturbances.Both species are of similar size and forage on similar prey species forcing them to share similar feeding niche 22 and being sympatric foragers, their overlap is mostly due the activity of their prey.The co-existence of these two species having overlapping diets and activity patterns suggests spatial segregation to reduce ecological overlap.The relatively low spatial overlap between species outside protected areas might correspond with habitat fragmentation in this area along with selection of jungle cats for human settlements 47 .
Similarly, the high overlap between the species in areas with presence of large predators present might be due to habitat quality 48,49 as well as providing foraging opportunities from carcasses left by them 22,41 .In addition, smaller felids can increase their activity during daily periods when top predators are less active to reduce risk 50,51 , resulting in increased activity overlap between leopard cats and jungle cats.Understanding these temporal patterns can aid in the development of conservation strategies promoting species coexistence.

Conclusions
Our study highlights interactions and activity patterns between jungle cats and leopard cats in a fragmented landscape.Co-existence of leopard cats and jungle cats appears to occur through a combination of spatial, and temporal differentiation.We recommend future studies consider diet analyses to better understand niche differentiation between these two species.Understanding these interactions between these species, among others, is important for ensuring integrity of ecosystems and associated process along with promoting coexistence with humans.

Study area
We conducted this research in the PKC (9661 km 2 ), Madhesh Province, Nepal, which encompasses the area between Parsa National Park (PNP) in the west and Koshi Tappu Wildlife Reserve (KTWR) in the east (Fig. 5).
In addition to protected areas such as PNP and KTWR, the PKC has community-managed and national and private forests that contribute to the conservation of over 50 mammalian species and the region's rich biodiversity 20,50,[52][53][54] .The PKC also serves as a crucial corridor for Asian elephants (Elephas maximus) migrating between PNP and KTWR 55 .
Elevations within PKC are 80-800 m above sea level.Major forest types are sub-tropical, including sal (Shorea robusta), and mixed forests dominated by acacia (Acacia catechu) species.Local communities residing in PKC rely on crop and livestock agriculture for their subsistence, and depend on forest products including firewood, leaves, and wood for various purposes 52,56 .

Data collection
We collected presence data for jungle cats and leopard cats during December 2022-March 2023.We deployed 154 cameras throughout PKC maintaining a minimum distance of 1 km between adjacent cameras, excluding areas with human settlements and open farmland (Fig. 5).We left cameras in place for 3 weeks, checking each week before moving each camera to another cell.Cameras were positioned 40-60 cm above ground along possible tracks and trails and each camera was programmed to obtain three images with a 30-s delay.The cameras were placed at random with respect to each other.We deployed cameras for 3234 trap days (154 sites × 21 days).We programmed cameras to take three images per detection with a 30-s delay between detections.
At each camera location we recorded canopy cover and distance to nearest waterbody, settlement, and major road.We extracted vector files for waterbody and major roads from OpenStreetMap 57 and the vector layer of human settlements from the Humanitarian Data Exchange 58 .We estimated canopy cover at each camera location using the Gap Light Analysis mobile application (GLAMA; 59 .Distance to nearest settlement, waterbody, and major road was measured using using QGIS. We identified all mammals (including humans) species from images.In addition to habitat parameters for each camera we also used number of livestock and human, and presence of large predators (tiger and leopard).Finally, we included the presence of leopard cat and jungle cat as a covariate for the other species.We standardized variables to have a mean of zero and standard deviation of 1 to account for heterogeneity 60,61 .

Data analysis
We first performed correlation analyses for continuous variables using a threshold of |r| > 0.7 62 .None of the variables were highly correlated (|r| ≤ 0.70); thus, all were used in analyses.We used hierarchical occupancy   63 to assess detection probability, naïve occupancy, and impacts of covariates on leopard cats and jungle cats.We used each week of 21 days each camera was deployed (total survey duration) as a sampling occasion representing three replicate occasions.We used two variable sets to model occupancy and detection probability.For occupancy (space use model) we used site-based factors including canopy cover percentage, large predator presence, sympatric cat species presence, number of humans detected, and number of livestock detected.For detection probability (detection model) we used distance-based factors including distance to water, distance to road, and distance to settlement.We hierarchically modeled occupancy and detection probabilities for each of the sympatric cat species.We used single species occupancy models for both species and created the object data as a matrix of species detections at each site i, where the matrix comprised the number of detections for each sampling replicate.We used occupancy as an indicator of habitat selection rather than performing spatial analysis [64][65][66] .We derived occupancy as where z is a latent variable that can be drawn from detection histories and z i is drawn from a Bernouli distribution with the parameter probability ψ.We then modeled detection probability as a binomial distribution where, if z i -1, p is the probability of success, and if z i -1, the probability of success equals zero (yi ~ Binomial(ni, pzi)).
Using ψ as the probability of occupancy, the equation for leopard cat was and the equation for jungle cat is given as where, β 0 = logit(ψ 0 ).And β varies for each species.We incorporated correlation between detection probability, occupancy, and intercept.β 0 is the probability of occupancy of the species at site i with a given combinations of variables 67 .
We generated model output using Markov Chain Monte Carlo (MCMC) simulation and confirmed model convergence by evaluating Rhat value, with a threshold of 1.1 68 .We ran the adaptive MCMC simulation using the jagsUI 69 and coda 70 packages in program R and Just Another Gibbs Sampler (JAGS; 71 ) with three chains, 1000 adaptations, 1000 burn ins and 15,000 iterations.We concluded the effect to be significant if the Bayesian credible intervals did not overlap 0. All means are reported with ± 1 standard deviation.
We derived a detection probability map of jungle cat and leopard cat occurrence using Inverse Distance Weighting (IDW) interpolation in QGIS using the tool "IDW Interpolation" 72 .We derived detection probabilities for each cell as the proportion of survey replicates in which the species was detected.We compared the difference in occupancy and detection probability of the species using t-test for both species.
We calculated activity overlap coefficient (Dhat1) of jungle cat and leopard cat using the overlap package 73 in R program.Overlap was calculated inside and outside protected areas as well as in presence and absence of large predators.Overlap ranges from 0 (no overlap) to 1 (complete overlap) and were used to calculate the extent of overlap between the respective kernel density estimates.We defined overlap as common area under the two curves by using the minimum of the two kernel estimate at each instant 74 .We used 999 bootstraps to generate 95% confidence intervals 75 .The calculated overlap was then compared using the overlapPlot function in the overlap package.

Figure 4 .
Figure 4. Diel detections of leopard cat (red line) and jungle cat (blue line) inside protected area (upper left), outside protected area (upper right), in presence of large predators (lower left) and in absence of large predators (lower right), Parsa-Koshi Complex, Nepal, 2022-2023.Dashed lines represent sunrise and sunset.